This document summarizes a study that segmented customers of a retail chain in India based on their purchase behavior data from loyalty cards and transaction records. The study analyzed over 300,000 transaction records and 13,000 loyalty card profiles. Key findings included: 1. Most valuable customers (purchasing over $500) made up only 3.3% of customers but marital status and membership tier impacted purchase amounts. 2. Customer purchases followed predictable patterns - higher value customers purchased more frequently, received larger discounts, and spent more per purchase. 3. Ten best-selling brands accounted for over 90% of items sold, showing the importance of focusing on top brands. The study aimed to identify customer segments

1. Segmenting Customers for Effective CRM – A Data Mining
Approach using Billing and Loyalty Card Data of a Leading
Retail Chain of Kolkata, India
Present affiliation of Authors
Dr. Atish Chattopadhyay
Professor of Marketing, SPJIMR, India
atishc@spjimr.org
And
Dr. Kalyan Sengupta
Professor of IT and Systems, IISW&BM, Kolkata, India
kalyansen2002@yahoo.co.uk
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2. Introduction
The past decade witnessed many changes in the approach towards marketing from
transaction orientation, marketing orientation (Narver and Slater, 1990; Kohli and
Jaworski, 1990), and mass customization to establishment of long term relationships with
customers (Webster 1992; Morgan and Hunt, 1994). Gronroos (1995) and Gummesson
(1987) strongly suggested that marketing is to establish, maintain and enhance relationships
with customers and other partners, at a profit, so that the objectives of the parties involved
are met. This could only be achieved through mutual exchange and fulfillment of promises
between the concerned parties. Eventually, customer relationship became the focus and
dominant paradigm of marketing.
It has also been suggested by many researchers that CRM is a philosophically related
offspring to relationship marketing (Zablah, Danny, and Wesley, 2003). The Academic
Community often uses the terms “Relationship Marketing” and CRM interchangeably
(Parvatiyar and Sheth 2001). Payne and Frow, 2005 reviewed the various definitions of
CRM and proposed the following definition “CRM is a strategic approach that is concerned
with creating improved share holder value through the development of appropriate
relationship with key customers and customer segments. CRM unites the potential of
relationship marketing strategy and IT to create profitable, long term relationship with
customer and key stake holders. CRM provides enhanced opportunities to use data and
information to both understand customers and co-create value with them. This requires a
cross functional integration of processes, people, operations and marketing capabilities that
is enabled through information, technology and applications.”
It may be observed from the above definition that the emphasis is on identification of key
customers and customer segments and to develop appropriate long term strategic
relationships to create a sustainable profit. It also emphasized on the need to integrate cross
functional processes. Payne and Frow, (2005) identified five key cross functional CRM
processes and emphasized the fact that CRM activities involve collecting and intelligently
using customer data to build a consistently superior customer experience and customer
2

3. relationship. They suggested that the strategy development process requires a focus on the
organizations business strategy and its customer strategy. Customer strategy involves
examining the existing and potential customer base and identifying which forms of
segmentation are most appropriate.
This paper aims to segment customers of a retail chain based on their observable store
specific characteristics like user status, usage frequency, store loyalty and patronage
(Wedel and Kamakura, 2000). The customer data of loyalty card holders and OLTP data of
bills were examined to classify customers for effective segmentation. The two retail outlets
(at the city of Kolkata) of a leading national level retail chain in India were used for the
purpose of the study.
Loyalty Programs as a CRM tactics
During the past decade, loyalty programs have been intensively experimented throughout
the globe mostly to create a new generation of CRM tactics as was evident from ample
experiences including Japanese retailing, US airlines and hotels, French banks, UK
groceries and so forth (Brown, 2000; Kalokota and Robinson, 1999; Field, 1997). In India
it was observed that Shoppers‟ Stop, a leading retail chain, managed to achieve 60 percent
of its sales from repeat customers (as against the Indian average of 30 percent) by virtue of
its highly pushed loyalty programs (Dasgupta 2005).
However, a group of researchers (Uncles et. al, 2003; Miranda et. al, 2004; Stauss et. al,
2005) observed from empirical researches that loyalty in repeat purchases is a result of
passive acceptance of brands rather than from positive efforts to improve customer
attitudes. A recent study (Noordhoff et. al, 2004) questioned the fate of loyalty programs in
the long run. Store customers of Netherlands and Singapore were compared in terms of
behavioral and attitudinal loyalty with respect to loyalty cards. It was concluded from the
study that efficacy of store loyalty programs appeared to diminish with an increasing
number of alternative card programs in the market. It also diminished with the habituation
of customer with these cards. While the sustainability of loyalty scheme is in question, the
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4. marketers need to be clear about relative importance of data collection and rewarding loyal
customers for achieving sustainable loyalty (Lisa O‟ Malley, 1998).
Understanding of appropriate factors which could build a cordon around the customers is
extremely essential. Organizational and regular feedback from the marketplace may
extract customers‟ latent needs in some ongoing manner. A well designed loyalty scheme
could be considered as a useful instrument for continuous tracking of customers, which
may enable a successful CRM and hence a sustainable loyalty improvement system. The
present study will address these issues in the Indian context with respect to a leading retail
chain in India.
Methodology
OLTP database of bills along with the customer database can generate enormous amount of
business intelligence in the customer process. Sequential steps applied for the purpose of
understanding customer characteristics and identification of key parameters influencing
sales, were as follows:
1. Copies of billing data from two retail points during the period August 2004 to February
2005, from the billing OLTP system of the retail chain were collected. The entire data
was dumped into a single ASCII data file, using fixed format space delimited structure.
There were altogether 59 different fields of different widths. Total number of records
were 3,34,093, each indicating a bill item.
2. Customer loyalty-card data was collected in a separate ASCII file. Data included
demographic and psychographic characteristics of customers, as collected on a form,
while registering individual customer with a loyalty card. There were 27 fields in the
file of different width. Total number of records was 12,990, each indicating one loyalty
card customer.
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5. 3. Data cleaning and Merging process was the most serious and time consuming task for
the project. In depth inspection of the data, so acquired, revealed a number of serious
difficulties, some of which may be listed below:
a) Name fields of customers were not properly designed. There were a number of sub-
fields like Title, First Name, Family Name, Final Name, etc. However, there was no
instruction manual available to fill in these fields. Frequent irregularies were found
in the data, as the sales persons while filling up forms, used their own discretions,
as and when required.
b) Address fields also created confusion (building, road, area, city, pin code, phone,
mail, call mailing, etc. were the name of the fields) resulting in wrong data in the
records.
c) Date of birth, Date of marriage, etc. were variable length string fields in the data-
file. Conversion of such data items into proper data fields required special
treatments.
d) Attributes of customers in the loyalty card form were also subject to confusion. For
example, race of the customer had options like Bengali, Marwari, Christians,
Muslims and others. Apparently, the options are not mutually exclusive – some are
origin of birth indicators and some are religion indicators. For the purpose of
clarity, the field was divided in two major groups – Bengali and Non-Bengali.
e) No indication of family income or economic status of Loyalty Card customer was
available.
f) Brands and sub-brands of items had no codification – these were only string fields.
g) Product category field was totally absent. For the purpose of our analysis, we
generated a field, indicating product category.
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6. 4. Cleaning and validation of the data set was a rigourous process. The data sets of bills
and customers were then merged together, finally for mining and further analyses.
(Figure 1).
Figure 1: Cleaning, Validation and Merging of Data
Cleaning,
Recoding and Preprocessed
Bill-data
Validation Data
Process
Data Set
Merge
For
Procedure
Mining
Cleaning,
Customer
Recoding and Preprocessed
Profile
Validation Data
Data
Process
5. It was necessary to estimate the customer value pyramid for our data set in order to
classify customers based on purchase value. Such customer classification was of critical
importance for a successful implementation of CRM.
6. The customer group was then segmented considering a few key behaviors of the
customers such as frequency, recency, bargain, average amount of purchase, etc.
6

7. Findings
1. Basic Distributions: Demographic and other basic distributions are listed in Table 1.
It was found that two third of card holders were married for both the shops. Male
population in the loyalty program was 57 percent on average. Gariahat shop was
dominantly patronized by Bangalis (75 percent), while the Camac Street shop had 55
percent Non-Bengalis. The Camac Street shop is located in the central business district
where population mix is highly heterogeneous.
Membership status is gives to a customer, depending on amount of purchase during the
time of registration. Volume of five star customers was only 6 percent while 84 percent
of total card holders had one-star status. Camac Street shop was much larger than the
other one and is situated at the central business area. This shop contributed for 87
percent of the loyalty members and the rest 13 percent by the other.
Table 1: Distribution of Basic Parameters
Parameter Percentage
Shop-wise Members
Camac Street 87
Gariahat 13
Membership Status
One Star 84.4
Three Star 9.4
Five Star 5.8
Gender
Male 57.6
Female 42.4
Marital Status
Married 71.8
Unmarried 28.2
Hour-wise Bills
12 – 4 PM 34.2
4 PM – 7 PM 36.7
7 PM – 8 PM 29.1
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8. In the loyalty card population as high as 72 percent were married. It was observed that
maximum sales occurred during January and February, when the shop offered its annual
sale (up to 50 percent discount). October is the festive month of the city and consequently
sale was considerably high.
It was noted that daily volume of sales (bill items) increased during afternoons and
highest during the evenings. Nearly 30 percent of sales occurred during a single hour
in the evening (7 - 8 pm), while during the early afternoon (12 - 4 pm) the volume per
hour was merely 8-9 percent.
Out of all the items sold in the shops, shirts, trousers, salwar-kameez, gift articles, shirts
ad stationeries were the high selling items. Accessories, cosmetics, kids and infant
apparels, linens etc. were also sold in abundant.
It was identified from the sales data that out of nearly 350 brands in the shop, 34 had
sales less than 5 pieces during the study period. 102 brands had sales 5-10 pieces and
176 brands has sales 11-50 pieces. Only 10 brands had frequent sales of 4000 pieces or
more (table 2).
Table 2: Sale of Top Ten Brands During the Study Period
(Brands sold more than 4000 pieces)*
Brand Percent Sold
RARE 27.2
ESKEE 12.1
LINCON 10.1
JOHN 9.6
MILT 9.2
RTKML 8.6
ARC 6.3
FRAAJILE 6.2
BALOON 5.4
TONMIL 5.2
TOTAL 100.0
* Brand names camouflaged
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9. 2. Customer Pyramid: It is interesting to note that out of 12990 loyalty customers, only
420 (3.3%) customers purchased more than INR 25,000 during a period of seven
months. These customers were most valuable patrons of the shops who might be
considered to be offered greater care and attention. The lowest category of the
customers, buying only less than INR5000 during the period, constituted 57 percent of
the customer volume (table 3). Nearly 16 percent customers, constituting the middle of
the pyramid, bought high values (between INR 10,000 to 25,000) and might be
considered for up-gradation.
Table 3: Customer Value Pyramid
Value Purchased Frequency of Percent Cumulative Percent
(INR) Customers
50K above 49 0.4 0.4
25K – 50K 371 2.9 3.2
15K – 25K 863 6.6 9.9
10K – 15K 1264 9.7 19.7
5K – 10K 2964 22.8 42.5
Less 5K 7449 57.3 100.0
Total 12960 99.8
3. Customer Value and Behaviors: Customer tracking and identifying their buying
behaviors was an important issue of the present paper. Initially the demographic
characters of the loyalty card customers were considered and these were related to the
customer pyramid. The estimated p-values of 2 tests and ANOVA test confirmed that
only membership status and marital status of card-holders had significant bearing on
the amount of purchase. Married customers appeared to buy more values, while Five-
Star customers also purchased more amount than others (Table 4). The other
demographic characters like age, sex and race did not show any impact on the customer
value even at 10 percent level of significance.
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10. Table 4: Effect of Demographic and other Characters on Customer Value
Variable Tested P-Value
Membership Status 0.00
Chi Square Test Sex 0.17
Marital Status 0.00
Bengali (race) 0.27
ANOVA Age 0.33
In order to take appropriate marketing decisions, it was important to estimate certain
activity patterns of the customers related to different categories of purchase values. The
activity parameters tested for the purpose were frequency, bargain amount, recency, and
average amount of purchase. Bargain amount was a derived variable, indicating difference
between MRP and bill value. It was evident from the ANOVA analyses that all these
variables, viz. bargain, recency, frequency and average amount of purchase were dependent
on value category of the customer pyramid (table 5). A further post hoc analysis explains
that the highest valued customers enjoyed maximum bargain amount, consistently lowering
with value categories of customers (table 4). Frequency of visit to the shop was again an
interesting observation to reveal that higher the values of customers, more frequent the
visits were during the study period. Also in case of average amount of purchase, one could
argue from the tests that, higher the values of the customers, higher the average amount of
purchase per visit. These interesting facts converge to some double benefit concept of the
customers. If the shop can succeed to bring in their customers more frequently, there
would be definite benefit of higher amount of average sales, resulting in higher customer
value at the end of the period.
Table 5: ANOVA Tables for Activity Based Variables
df Mean Square F Sig.
Bargain Between Groups 5 1495998635.1 827.206 0.000
Within Groups 12923 1808496.854
Total 12928
Recency Between Groups 5 1213752.323 347.555 0.000
Within Groups 12903 3492.264
Total 12908
Frequency Between Groups 5 10022.528 1784.242 0.000
Within Groups 12941 5.617
Total 12946
Avg. amt Between Groups 5 2841779561.7 789.478 0.000
Within Groups 12941 3599569.312
Total 12946
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11. 4. Customer Value Characteristics: The longitudinal purchase data of customers were
quite useful to explore customer behaviors during the period of study. Demographic
and activity based variables were used to demonstrate value purchased by the
customers. A series of regression models and artificial neural network models were
applied to perform such causal analysis. In all the predictive models value purchased
during the period was the dependent variable. Independent variables were available
demographic and activity variables, viz., frequency, recency, bargain, age, gender,
marital status, shop code, race type etc.
A step-wise regression model on the data set confirmed (R Square value of 0.52) the
fact that amount of purchase by a customer was strongly and positively related to
frequency of visit to the shop and bargain-amount (difference between MRP and bill
value) the customer enjoyed from the shop (table 6). However, recency of a customer
was also positively related with much lower impact. The standardize Beta-coefficients
were 0.528, 0.350 and 0.035 respectively. However, t-values of the entire regression
coefficient showed high significance, pleading robustness of all the models. It was
interesting to note that the dummy variables gender (female = 1) and type (non-bengali
= 1) had negative low impacts on the customer revenue. These results indicated that
male members purchased more than the female members. Also Bengalis purchased
more amounts than non-bengalis. The final regression model had high F-value of 2799
indicating significance level of 000. The model did not however, include other
demographic variables like age, marital status etc.
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12. Table 6: Stepwise Regression – Both Shops
Model Unstandardized Coefficient Standardized t Sig.
Coefficient
B Std. Error Beta
1 (Constant) 1097.858 77.199 14.221 0.000
Frequency 1626.298 16.902 0.649 96.219 0.000
(Constant) 836.337 70.442 11.873 0.000
2 Frequency 1276.345 16.826 0.510 75.857 0.000
Bargain 1.753 0.034 0.345 51.324 0.000
(Constant) 1076.600 77.169 13.951 0.000
3 Frequency 1291.442 16.908 0.516 76.382 0.000
Bargain 1.745 0.034 0.343 51.195 0.000
Gender -750.892 99.560 -0.047 -7.542 0.000
(Constant) 614.180 117.528 5.226 0.000
Frequency 1326.465 18.178 0.530 72.973 0.000
4 Bargain 1.773 0.034 0.349 51.445 0.000
Gender -722.547 99.606 -0.045 -7.254 0.000
Recency 4.502 0.864 0.036 5.213 0.000
(Constant) 763.921 130.619 5.848 0.000
Frequency 1322.731 18.229 0.528 72.562 0.000
5 Bargain 1.777 0.034 0.350 51.524 0.000
Gender -722.889 99.583 -0.045 -7.259 0.000
Recency 4.321 0.866 0.035 4.989 0.000
Type -252.771 96.306 -0.016 -2.625 0.009
Dependent Variable: Total bill value of a customer
In order to compare characteristics of the two different shops – one located at Camac Street
and the other at Gariahat. Regression models were applied to perform such causal analysis.
In all the predictive models value purchased during the period was the dependent variable.
Independent variables were available demographic and activity variables, viz., frequency,
recency, bargain, age, gender, marital status, shop-code, race, type, etc.
Table 7 illustrates the regression models for the Gariahat shop. It is interesting to observe
that only activity based variables (viz. frequency, bargain and recency) were the
independent variables chosen by the models and demographic variable was entered.
Importance of the activity variables was similar to the previous models for both shops.
This indicates that basic buying behaviors are to a large extent similar in two cases.
The results of the Camac Street shop (which is located is a central commercial area of the
city), although indicated a similar relationships in terms of activity based variables. But in
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13. this case two demographic variables (viz. gender and race type) were also entered in the
model, though impact of these variables was merely marginal.
Thus the shopping behavior of customers of two different outlets of the chain were mostly
the same and marginally different because of location factor. Two sets of regression models
confirmed that only three variables (frequency, bargain and recency) could explain amount
of purchase in case of Gariahat shop whereas two extra variables (gender and type) were
also useful to predict the purchase value of customers for the Camac Street shop.
Table 7: Stepwise Regression - Gariahat Shop
Model Unstandardized Coefficient Standardized t Sig.
Coefficient
B Std. Error Beta
1 (Constant) 898.196 162.659 5.522 0.000
Frequency 1479.870 38.259 0.663 38.680 0.000
(Constant) 718.403 151.115 4.754 0.000
2 Frequency 1244.466 37.877 0.558 32.855 0.000
Bargain 1.767 0.100 0.300 17.693 0.000
(Constant) 46.099 256.662 0.180 0.000
3 Frequency 1302.100 41.770 0.584 31.173 0.000
Bargain 1.824 0.101 0.310 18.029 0.000
Recency 6.148 1.900 0.060 3.236 0.001
Dependent Variable: Total bill value of a customer
Shop – Code: Gariahat
A further analysis on the two outlets showed that the top cluster of the customer group of
Camac Street had much higher average purchase value of INR 75760 during the period
under study, while it was INR 44380 at the Gariahat outlet. It was interesting to note that
for both the shops, frequency of visits and the amount of bargain were the two most
important factors for total amount of purchase.
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14. Table 8: Stepwise Regression - Camac Street Shop
Model Unstandardized Coefficient Standardized t Sig.
Coefficient
B Std. Error Beta
1 (Constant) 1153.637 85.959 13.421 0.000
Frequency 1644.660 18.602 0.648 88.414 0.000
(Constant) 863.243 78.436 11.006 0.000
2 Frequency 1281.528 18.573 0.505 68.999 0.000
Bargain 1.745 0.037 0.347 47.433 0.000
(Constant) 1119.015 85.901 13.027 0.000
3 Frequency 1297.378 18.659 0.511 69.531 0.000
Bargain 1.737 0.037 0.346 47.319 0.000
Gender -803.974 111.388 -0.048 -7.218 0.000
(Constant) 683.233 129.567 5.273 0.000
Frequency 1329.873 19.998 0.524 66.500 0.000
4 Bargain 1.762 0.037 0.351 47.497 0.000
Gender -776.810 111.454 -0.047 -6.970 0.000
Recency 4.300 0.958 0.034 4.490 0.000
(Constant) 881.868 146.781 6.008 0.000
Frequency 1324.895 20.066 0.522 66.026 0.000
5 Bargain 1.766 0.037 0.351 47.586 0.000
Gender -774.438 111.419 -0.047 -6.951 0.000
Recency 4.089 0.960 0.032 4.259 0.000
Type -311.117 108.154 -0.019 -2.877 0.004
Dependent Variable: Total bill value of a customer
Shop – Code: Camac Street
Artificial Neural Network (ANN) Model
A Neural Network based Multi-layered Perception model was tried on the data with
customer bill amount as predicted variable and eight input variables namely frequency,
gender code, recency, shop code, type (Bengali or non-bengali), age, bargain, marital state.
It was found that the model could generate a prediction with 96 percent accuracy, using 1:3
neurons Hidden Layers.
The estimated relative importance of the input variables is varied in nature, where bargain
being the most important factor, followed by frequency and recency. The least important
factors were shop code, type of customer and marital status of customers.
It is interesting to note that in both the models (regression and ANN) both bargain and
frequency were important parameters while frequency was more important as judged by
14

15. regression model unlike ANN model. Recency was important to both the models. Marital
status was however considered to be not important in both the models.
Figure 2: Results of Neural Modeling using Clementine 9.0
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16. K-Means Cluster
A K-Means cluster analysis was performed on the data set with eight variables namely:
value of purchase, frequency of visit, race, age, time of store visit, marital status, gender
and store location. It was observed that the model could generate six clusters as shown in
figure 3 below.
Figure 3: Results of K-Means Clustering using Clementine 9.0
16

17. It is interesting to note that cluster 2 consisted of the maximum number of members, all
whom were Bengalis and married. The value of purchase and the frequency of visit for this
segment was also the highest and all of them happened to shop at the Camac Street Store.
On the other hand cluster 3 comprised of members who were all non-Bengalis and all of
whom were male. It may be further observed that the members were mostly young and
middle aged, frequented the stores between ten to fifteen times over the period of eight
months and shopped during the evenings. The details of the six customer clusters are given
in Table 9 below.
Table 9: Customer Clusters and their Attributes
Cluster 1 2 3 4 5 6
No. of Members 2295 2808 2646 2046 2454 2124
Value of purchase 4800 6600 5900 6000 4200 5600
(Rs.)
Freq. of visit 10 15 13 13 11 15
Race (Bengali %) 40 100 0 60 100 0
Age Young- Young- Young- Young- Young- Young-
middle middle middle middle middle middle
Time of store visit Late Eve. Eve. Eve. Eve. Eve. Eve.
Marital Status 0 100 100 0 92 97
(Married %)
Gender (Male %) 97 58 100 50 52 0
Store (Camac 68 100 81 83 0 80
St.%)
It may be noted that that the variables considered were those which were available in the
„Application Form‟ designed by the shop management.
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18. Managerial Implications
It was observed that the form for collection of customer data by the retail store had certain
short comings owing to design problems which created confusion amongst those filling up
the same. Vital data regarding demographic and psychographic profile of the customers
could not be collected as the same was not included in the form. Non-availability of a
standard instruction manual created further complications for the front line sales force. The
front line sales force was also not adequately trained for the purpose. These points out the
importance of proper design of the instrument for data collection and also need for
adequate training of the front line sales personnel who plays an important role in the
process of data collection.
Analyses of billing database and customer profile enabled classification of customers and
identification of key customers of the stores based on purchase value. The categorization of
customers and creation of a customer pyramid is vital in designing effecting strategies for
each of the customer groups. This would also enable identification of opportunities for
upward migration in the pyramid and retention strategies.
Factors influencing sales for the customer groups revealed that factors like frequency of
visit and bargain or discount offered to customers had a strong positive impact on sales.
This implies that it is critical for the store to make people visit the store more frequently.
Also sales promotions are critical for the store to increase sales than other forms of
promotion. Marketing investments may be aligned based on the above findings.
Recency had a low but positive impact on the amount of sales. It was extremely useful to
maintain a low average recency for the customers and management actions may be devised
to target, follow-up and encourage those customers whose recency values are above the
expected threshold. Also those customers who had not visited the store in the recent past
may be tracked to ascertain the reasons for not patronizing the store.
18

19. The customer data may also be effectively utilized to segment shoppers and specific tactics
employed to develop these segments.
Directions of Future Research
This research was conducted based on the data of the actual purchases made by the
members of the loyalty card program over a period of time. However, the original member
of the loyalty card program may not always be the buyer as the card is more of a family
card. No research was undertaken to study the attitude of the buyers who actually used the
loyalty card, which might reveal valuable information. Such researches may be undertaken
in the future.
In this case the customer was segmented based on observable store specific parameters.
Further research may be undertaken to segment the customers based on cultural,
demographic and socio-economic variables using the customer data as collated in the
membership forms of the holders of the loyalty cards.
Further studies may be carried out to track the movement of brands in the store. Research
may be undertaken to analyze the market basket both on product categories as well as
brands. It may also be of interest to study the choice of product categories as well as brands
for each of the customer groups which may be used to design appropriate promotional
strategies.
Conclusion
Loyalty programs can become means for earning valuable customer information to shape
up appropriate marketing strategies. Loyalty cards as is evident can generate a sizeable
amount of valuable customer data which enables to track and monitor customers in an
effective way in the process of co-creation of value, which is a crucial component of CRM.
It may enable the organization to focus on the most profitable customers and customers
segments.
19

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